Correspondence
Bibliographic record
Abstract
Todorich, Bozho MD, PhD1; Thanos, Aristomenis MD1; Yonekawa, Yoshihiro MD1; Thomas, Benjamin J. MD1; Faia, Lisa J. MD1; Chang, Emmanuel MD2; Shulman, Julia MD3; Olsen, Karl R. MD4; Blair, Michael P. MD5; Shapiro, Michael P. MD5; Ferrone, Philip MD6; Vajzovic, Lejla MD7; Toth, Cynthia A. MD7; Lee, Thomas C. MD8; Robinson, Joshua MD9; Hubbard, Baker MD9; Kondo, Hiroyuki MD10; Besirli, Cagri G. MD, PhD11; Nudleman, Eric MD, PhD12; Wong, Sui Chien MD13,14,15; Kusaka, Shunji MD16; Walsh, Mark MD, PhD17; Chan, R. V. Paul MD18; Berrocal, Audina MD19; Caputo, George MD20; Murray, Timothy G. MD, MBA21; Sears, Jonathan MD22; Schunemann, Roberto MD23; Harper, Clio A. III MD24; Kychental, Andres MD25; Dorta, Paola MD26; Cernichiaro-Espinosa, Linda A. MD27; Wu, Wei-Chi MD, PhD28,29; Campbell, J. Peter MD, MPH, MA30; Martinez-Castellanos, Maria A. MD31,32; Quiroz-Mercado, Hugo MD32; Hayashi, Hidyuki MD33; Quiram, Polly MD, PhD34; Amphornphruet, Atchara MD35; Hartnett, Mary E. MD36; Tsui, Irena MD37; Ells, Anna MD38; John, Vishak MD39; Moshfeghi, Darius MD40; Capone, Antonio Jr MD1; Drenser, Kimberly A. MD, PhD1; Trese, Michael T. MD1 Author Information
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.709 | 0.485 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".